Detection of Epileptic Seizures Through Audio Classification

Bruijne, GR de; Sommen, P.C.W.; Aarts, RM Ronald

doi:10.1007/978-3-540-89208-3_344

Cited by 16 publications

(20 citation statements)

References 5 publications

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“…subtle face or hand movement during partial seizures, or behavioral arrest. Brune et al [6] used audio for detecting lip smacking; however performance was among the poorest of all the systems that we evaluated (F-score = 0.04). There is a need for improving systems.…”

Section: Limited Diversity Of Seizure Typesmentioning

confidence: 89%

“…Much less work has been done to explore seizure detection with other types of sensors. Bruijne et al analyzed [6] audio for detecting ''lip smacking'' and ''screams'' however; performance was poor due to considerable variation among patient vocalizations (average F-score = 0.250). Van Elmpt et al [19] used ECG measurements for detecting the onset of heart rate changes associated with seizures and achieved competitive performance with inertial sensors (F-score = 0.391).…”

Section: Audio Ecg Emg Pressurementioning

confidence: 99%

“…The resulting tables can then be used for more closely examining system performance with respect to under and over reporting. High recall systems with low precision [6,49] seldom miss seizures for addressing the concern of underreporting yet tend to overcompensate and over report seizures due to false alarms. High precision systems with low recall [1922] have the opposite problem and address the concern of overreporting seizures at the risk of missing seizures.…”

Section: Self-reporting Comparisonmentioning

confidence: 99%

“…More work is needed for reducing false positives among all classes that we surveyed. Table 2 shows that many of the best performing systems utilize video with an average F-score of 0.79 (SD = 0.29) while audio based systems performed the worse with an average F-score performance of 0.25 (SD = 0.30) with precision as low as 2% for detecting audible lip smacking [6]. Inertial systems are shown to perform well across both day and nighttime studies [2,49], however as noted more work must be done for reducing false alarms during daily activities [62].…”

Section: Inaccurate Seizure Counts For Aed Adjustmentmentioning

confidence: 99%

See 3 more Smart Citations

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies

Bidwell

Khuwatsamrit

Askew

et al. 2015

Seizure

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This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use.

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Section: Limited Diversity Of Seizure Typesmentioning

confidence: 89%

Section: Audio Ecg Emg Pressurementioning

confidence: 99%

Section: Self-reporting Comparisonmentioning

confidence: 99%

Section: Inaccurate Seizure Counts For Aed Adjustmentmentioning

confidence: 99%

See 2 more Smart Citations

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies

Bidwell

Khuwatsamrit

Askew

et al. 2015

Seizure

View full text Add to dashboard Cite

show abstract

“…Most remote CS detection methods reported in literature are, like ours, targeted on movement periodicity (the exception being methods targeted on seizure sounds and muscle activity). CS have been detected in video recordings by calculating periodicity in the luminance signal and with neural networks trained on optical flow motion tracking output .…”

Section: Discussionmentioning

confidence: 99%

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

et al. 2018

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People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

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Convolutional neural network for detection and classification of seizures in clinical data

Iešmantas

Alzbutas

2020

Med Biol Eng Comput

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Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate of false positives has been achieved with commercially available seizure detection tools, which usually are patient non-specific. Epilepsy patients suffer from severe detrimental effects like physical injury or depression due to unpredictable seizures. However, even in hospitals due to the high rate of false positives the seizure alert systems are of poor help for patients as tools of seizure detection are mostly trained on unrealistically clean data, containing little noise and obtained under controlled laboratory conditions, where patient groups are homogeneous, e.g. in terms of age or type of seizures. In this study authors present the approach for detection and classification of a seizure using clinical data of electroencephalograms and a convolutional neural network trained on features of brain synchronisation and power spectrum. Various deep learning methods were applied, and the network was trained on very heterogeneous clinical electroencephalogram dataset. In total, eight different types of seizures were considered, and the patients were of various ages, health conditions and they were observed under clinical conditions. Despite this, classifier presented in this paper achieved sensitivity and specificity equal to 0.68 and 0.67, accordingly, which is a significant improvement as compared to the known results for clinical data.

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Detection of Epileptic Seizures Through Audio Classification

Cited by 16 publications

References 5 publications

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies

Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies

Automated video‐based detection of nocturnal convulsive seizures in a residential care setting

Convolutional neural network for detection and classification of seizures in clinical data

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